1. Data Security and Privacy Keke Chen
Cloud Computing
Data Security and Privacy
Keke Chen

2. Outline Data confidentiality Data privacy access pattern privacy
Integrity of cloud processing

3. Private data in cloud computing
To use cloud services, the users may have to outsource data
Except for public services, such as maps, search engine
Data can be relational databases, multidimensional vector data, graph data, text data, images, video…

4. Problem with outsourced data
Data confidentiality
Threat model
External attackers
encryption is sufficient
Internal attackers
Cannot work on data encrypted with existing techniques
New methods are needed

5. Problem Access pattern
queries, query results, and how the query is processed
the user’s query might be private
The returned data, the access path may reveal data distributions – used to compromise data confidentiality
Threat model
Internal attackers

6. Problem Query correctness Is the returned result the correct result?
Example:
Outsourced database services
content distribution network
Threat models
Malicious service providers
Honest but lazy service providers

7. Normal Assumption on the attackers
Honest but curious service providers
Honest – do what you want it to do
Curious – want to peek at your information
Applies to data confidentiality and access pattern
Query correctness assumes dishonest providers
To reduce the service cost
Intentionally change content

8. Data confidentiality
The ultimate solution: fully homomorphic encryption
Computation on encrypted data directly, without the need to decrypt the data
Example
E(X) ◊ E(Y) = E(X+Y) : Pailier
E(X) ◊ E(Y) = E(X*Y) : ElGamal, RSA
Both ‘+’ and ‘*’: Gentry’s method (Gentry 2009)
Problem: efficiency
google search with encrypted keywords and FHE would increase the cost by about a trillion times

9. Data confidentiality
keyword search for encrypted documents (Song 2000)
Basic idea
Linear scan through the text and try to verify whether each encrypted word is matched
Only the searched word can be correctly decrypted.
Other words will generate random code
Problem
Linear scan, not efficient for large data
Privacy of search is not preserved – the service provider knows
Special documents might be identified

10. Secure keyword search Song 2000 (paper 186)
Seed is random, different for
each Wi
Key idea: Li and Ri are self-
verifiable
Advantage of XOR

12. How to set K?

13. Data confidentiality
Public-key keyword search and range search (Shi 2007)
Scenario: store encrypted data in public storage. Only authorized users can access the authorized part of the data
Example
Audit log: authorized users access certain days of the log (range query)
Problem: key management
The range of a searchable dimension is discretized
Each value needs a pair of public-private keys
Still linear scan

14. Data confidentiality For many data intensive applications Problem
Linear scan is not sufficient
Need indices to make search faster, for example
OLAP
k nearest search in geological/multimedia databases
Problem
Indexable encrypted data

15. Data confidentiality Indexable encryption on relational data
Order preserving encryption
Map a column of data to arbitrary distribution, while preserving the value order
Able to build index on the column (order is preserved)
Easy to translate queries to the encrypted data
Crypto-index
Map a column of data to buckets
Give random ID to the buckets
Values are represented with the bucket IDs
Range search becomes a search on a list of bucket IDs

16. Problems with OPE and crypto-index
Weak threat model: assume attackers have no knowledge about data distribution
Distributional attack
Assume the attacker knows the column distribution: easily map OPE encrypted column back to the original column
Crypto-index: Attacking queried ranges  derive the mapping between bucket-id and real value range
Return junk records (crypto-index)
to hide the column distribution, have to make each bucket equi-height (i.e., same number records in each bucket)

17. Weaker data confidentiality
Privacy protection
Do not hide the complete information
Attackers may estimate but cannot get exact values
Data utility is easier to preserve
Techniques
Data perturbation
Data anonymization

18. Data perturbation Definition Techniques
1. randomly change the original data
2. the attacker cannot effectively recover the original data
3. the desired properties are preserved
Techniques
Single dimension: noise addition
Multidimensional
Geometric perturbation
Random projection

19. Noise addition Y = X+ R Applications in data mining Attacks
X: original data column, R: random noise (distribution published), Y: published data
Applications in data mining
Reconstructing column distribution
Rakesh Agrawal SIGMOD 2000
Applied to privacy-preserving decision tree, naïve bayes classifier
Attacks
Spectral filtering (Kargupta ICDM 2004)
PCA reconstruction (Huang SIGMOD2005)

20. Geometric data perturbation
Y=RX+T+D
R: secret rotation matrix (preserve Euclidean distances)
T: secret random translation matrix, D: secret random noise matrix
Distances are approximately preserved (D)
Resilient to most attacks to rotation perturbation
Applications
Outsourced privacy preserving data mining, applicable for many classification and clustering algorithms
Attacks
Population based attacks (when covariance matrix is revealed)

21. Random Projection Y=AX+D Applications Attacks
A: random projection, e.g., entries from N(0,1)
Distances are approximately preserved
Applications
Many classification and clustering algorithms
Worse accuracy than geometric perturbation
Good for sparse high-dimensional data (text data), i.e., sketch methods (A is randomly generated for EACH record)
Attacks
Possibly more resilient than other two perturbation methods

22. Random Space Perturbation
y = A( E(x), 1, v)T
x is a k-D vector, E(x): order preserving encryption, v: random value
A : (k+2)x(k+2) random invertible matrix
Properties
Preserve half space queries, e.g., f(x)<0
Does not preserve distance, orders, (resilient to the known attacks to OPE, GDP, etc.)
Applications: range query, data mining (linear classifier + boosting)

23. Data anonymization Quasi Identifier anonymization problem
Governor of MA uniquely identified using ZipCode, Birth Date, and Sex.
Name linked to Diagnosis
87 % of US population
Name
SSN
Visit Date
Diagnosis
Procedure
Medication
Total Charge
Name
Address
Date Registered
Party affiliation
Date last voted
Zip
Birth date
Sex
Zip
Birth date
Sex
Quasi Identifier
Voter List
Medical Data
Sweeney, IJUFKS 2002 23

24. Basic problems in anonymization
Linkage between
Records
Attributes
Tables
Attacker’s prior knowledge and published data
Current research
address possible data linkage attacks
Propose techniques to prevent them
A nice survey paper

25. Differential privacy Statistical database Perturb the query results
Allow users to submit aggregate queries
But need to protect from query inference attacks
Perturb the query results
Cynthia Dwork ( proposed differential privacy
A method for designing the random noise for user-specific “privacy budget” and specific aggregate functions.

26. Privacy of access pattern
Do not want the attacker know what items the user is accessing
Known access pattern can be used to derive a lot of private information
Example:
AOL search log  identify real person  what sensitive keywords he/she used
naive solution: download the entire database 

27. Privacy of access pattern
Private information retrieval (PIR)
Basic idea: hide the real access pattern among a lot of accesses
Multi-server information theoretic PIR (Chor 98)
Computational PIR – same assumption as computationally secure encryption (Kushilevitz 97)
Problem:
High communication cost: need to transfer a lot of contents
Most efficient solution still needs O(n1/2) communication cost

28. Query correctness check whether server faithfully returns the results
Challenge token (Sion 2005)
put a challenge query (that the user knows the answer) among a batch of b queries
This solution only provides a probabilistic guarantee on query correctness

29. Use Merkle hash tree (Li 2006)
Integrate Merkle hash tree with B-tree index
For each search the service provider need to provide a hash value to prove the right access path was used
Client can verify the hash with its own merkle hash tree

30. Proof of retrievability
Problem
Archived or backup data in the cloud, sporadically accessed
Make sure the data is still there (not discarded by cloud provider for saving money)
Make sure the data is not modified
Approaches
Similar to challenge based methods